In this paper we apply several computational intelligence techniques to the problem of bankruptcy prediction of medium-sized private companies. Financial data was obtained from Diana, a large database containing financial statements of French companies. Classification accuracy is evaluated for Linear Genetic Programs (LGPs), Classification and Regression Tress (CART), TreeNet, and Random Forests, Multilayer Perceptron (using Back Propogation), Hidden Layer Learning Vector Quantization and several gradient descent methods, conjugate gradient methods, the Levenberg-Marquardt algorithm (LM), the Resilient Backpropogation Algorithm (Rprop), and One Step Secant Method. We analyze 2 datasets, one is balanced and the other unbalanced. TreeNet has the best performance accuracy on unbalanced dataset and LGPs performs the best on balanced dataset. Scaled Conjugate Gradient performs the best among the neural network training functions used for the balanced dataset; and Resilient Back Propagation performs the best among the training functions used for the unbalanced dataset. Our results demonstrate the great potential of using computational intelligent techniques, as an alternative to discriminant analysis, in addressing important economics problems such as bankruptcy prediction.
This research proposes an inference model fuzzy to analyses hazardous environmental work conditions, specifically insalubrious work conditions relevant for heat risk to support the safety engineers for making decisions. The article presents a study that consists of a fuzzy inference model specification for the evaluation of heat agents. The structure of model fuzzy used are inputs temperature and metabolic rate, while the output is work environment condition that could be salubrious or insalubrious. Through, the inference method Mamdani and rules established according to Brazil legislation about heat, Occupational Hygiene Standard 06, the proposed model can determine the work conditions about the heat. The validation process is done in an industry from the Industrial Pole of Manaus, therefor all the process necessary for preparations to use the proposed model is described to obtain all the variables necessary in the field. As a result, the proposed model got the correct classification of work environment conditions with pertinent results according to current legislation and technical expertise.
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